Estimating underdiagnosis of COVID-19 with nowcasting and machine learning

Garcia, Leandro Pereira; Gonçalves, André Vinícius; Andrade, Matheus Pacheco de; Pedebôs, Lucas Alexandre; Vidor, Ana Cristina; Zaina, Roberto; Hallal, Ana Luiza Curi; Canto, Graziela De Luca; Traebert, Jefferson; Araújo, Gustavo Medeiros de; Amaral, Fabiana Mortimer

doi:10.1590/1980-549720210047

Cited by 4 publications

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References 36 publications

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“…By harnessing the power of machine learning and artificial intelligence, it is possible to offer an alternative to standard biochemical diagnostic tools, such as reverse transcription-polymerase chain reaction (RT-PCR) assays, which in some parts of the world remain expensive and difficult to obtain ( 60 ). It may also be useful in analysing a dynamic of current clinical state of patients, and on this basis nowcasting the further disease development ( 61 ).…”

Section: Discussionmentioning

confidence: 99%

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

et al. 2022

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BackgroundSince the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.MethodsBiochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman’s rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.ResultsThe parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.ConclusionThe current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

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Section: Discussionmentioning

confidence: 99%

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

et al. 2022

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Ladybug Beetle Optimization algorithm: application for real-world problems

Safiri

Nikoofard

2022

J Supercomput

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In this paper, a novel optimization algorithm is proposed, called the Ladybug Beetle Optimization (LBO) algorithm, which is inspired by the behavior of ladybugs in nature when they search for a warm place in winter. The new proposed algorithm consists of three main parts: (1) determine the heat value in the position of each ladybug, (2) update the position of ladybugs, and (3) ignore the annihilated ladybug(s). The main innovations of LBO are related to both updating the position of the population, which is done in two separate ways, and ignoring the worst members, which leads to an increase in the search speed. Also, LBO algorithm is performed to optimize 78 well-known benchmark functions. The proposed algorithm has reached the optimal values of 73.3% of the benchmark functions and is the only algorithm that achieved the best solution of 20.5% of them. These results prove that LBO is substantially the best algorithm among other well-known optimization methods. In addition, two fundamentally different real-world optimization problems include the Economic-Environmental Dispatch Problem (EEDP) as an engineering problem and the Covid-19 pandemic modeling problem as an estimation and forecasting problem. The EEDP results illustrate that the proposed algorithm has obtained the best values in either the cost of production or the emission or even both, and the use of LBO for Covid-19 pandemic modeling problem leads to the least error compared to others.

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Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic

Khalilpourazari

Doulabi

Çiftçioğlu

et al. 2021

Expert Systems with Applications

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This research proposes a new type of Grey Wolf optimizer named Gradient-based Grey Wolf Optimizer (GGWO). Using gradient information, we accelerated the convergence of the algorithm that enables us to solve well-known complex benchmark functions optimally for the first time in this field. We also used the Gaussian walk and Lévy flight to improve the exploration and exploitation capabilities of the GGWO to avoid trapping in local optima. We apply the suggested method to several benchmark functions to show its efficiency. The outcomes reveal that our algorithm performs superior to most existing algorithms in the literature in most benchmarks. Moreover, we apply our algorithm for predicting the COVID-19 pandemic in the US. Since the prediction of the epidemic is a complicated task due to its stochastic nature, presenting efficient methods to solve the problem is vital. Since the healthcare system has a limited capacity, it is essential to predict the pandemic's future trend to avoid overload. Our results predict that the US will have almost 16 million cases by the end of November. The upcoming peak in the number of infected, ICU admitted cases would be mid-to-end November. In the end, we proposed several managerial insights that will help the policymakers have a clearer vision about the growth of COVID-19 and avoid equipment shortages in healthcare systems.

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Estimating underdiagnosis of COVID-19 with nowcasting and machine learning

Cited by 4 publications

References 36 publications

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Ladybug Beetle Optimization algorithm: application for real-world problems

Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic

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